Machine Learning on Big Data: From Statistical Learning to Probabilistic Modeling

Dear Colleagues,

Artificial Intelligence (AI) is an enormous field, while it can be defined as automating thinking processes using computers where the paradigm shift from diagnosis to prognosis. To help machines act like human beings, one of the latest trend is gaining insights from a large amount of data, and finally, to make decisions under dynamic and uncertain circumstances. Machine Learning (ML) on Big Data not only complemented statistical analysis, but also moving towards probabilistic modeling and optimization under blackbox scenarios. Articles that generate intelligence in terms of environment perception, problem cognition, to decisions formulation are called for in this special issue. Fields revolutionized by AI and ML, including cybersecurity, health care, traffic & transportation, warehousing, logistics, engineering & manufacturing, finance, marketing, and others are all welcome. Finally, the scope of this special issue covers, but not limited to the following:

* Detection & prediction techniques using Machine Learning
* Hyper-parameter tuning through Bayesian optimization
* Uncertainty quantification of Machine Learning
* Machine Learning on non-Euclidean data
* Generative modeling using Deep Learning
* Deep reinforcement learning for control and decisions
* Design of experiments enhanced by Bayesian optimization
* AI-driven optimization leveraging ML to improve optimization methods
* Multi-agent systems and game-theoretic intelligence

Prof. Dr. Ching-Shih Tsou
Dr. Duxin Chen
Guest Editors

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